FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering

Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding


Abstract
We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.
Anthology ID:
S17-2052
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
320–325
Language:
URL:
https://aclanthology.org/S17-2052
DOI:
10.18653/v1/S17-2052
Bibkey:
Cite (ACL):
Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, and Zhaoyun Ding. 2017. FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 320–325, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering (Zhang et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2052.pdf